Poor prenatal care in an urban area: A geographic analysis

ARTICLE IN PRESS Health & Place 15 (2009) 412–419

Contents lists available at ScienceDirect

Health & Place journal homepage: www.elsevier.com/locate/healthplace

Poor prenatal care in an urban area: A geographic analysis He´le`ne Charreire a,, Evelyne Combier b a b

Medicine, Science, Health and Society Research Center (CERMES), CNRS 8169, INSERM U750, School for Advanced Studies in Social Sciences (EHESS), 94801 Paris, France Department of Medicine, Population Epidemiology Center (CEP), University of Burgundy, France

a r t i c l e in f o

a b s t r a c t

Article history: Received 2 November 2007 Received in revised form 7 July 2008 Accepted 16 July 2008

Poor prenatal care increases the risk of having a premature or low-birth-weight infant. Rates of poor prenatal care vary spatially, influenced not only by individual mothers’ characteristics but also by social neighborhood context and proximity to healthcare services. The aim of this article is to identify and map the spatial patterns of prenatal care and to analyze the spatial and social origins of such inequalities. Our study concerns 30,338 individuals who received antenatal care in a highly urbanized French district: Seine-Saint-Denis. The geographical distribution of poor prenatal care is revealed by exploratory spatial data analysis tools. This spatial clustering is related to the contextual characteristics of neighborhoods (deprivation index). For this purpose a geographic information system is used, in conjunction with a field survey. The analyses and the survey reveal local particularities that hinder the take-up of healthcare services by pregnant women. & 2008 Elsevier Ltd. All rights reserved.

Keywords: Perinatal health inequality Access to health care Social determinant Spatial analysis France

Introduction In France health disparities are often highlighted in rural areas and related to urban/rural differences in at-risk behaviors, socioeconomic profiles and rapidity of access to emergency services. Irrespective of the environment, urban areas are generally perceived to be places where everything is accessible (owing to the concentration of human and material potential). Yet in the health field, proximity does not systematically imply accessibility. Lack of health insurance, no regular income, long waiting periods, the impossibility of communicating in cases where the individuals do not speak French, unsuitable consulting hours and inadequate means of transport make some facilities less accessible than others, depending on the users’ social and cultural characteristics. Most research on access to healthcare focuses on individual social determinants such as income, education and socio-economic status (SES) (Nelson et al., 1999; Blondel and Marshall, 1998; Delvaux et al., 2001). But awareness has recently developed of the role of contextual social factors such as barriers to access to health services, and the importance of taking these factors into account to explain any difficulties of access (Law et al., 2005; Kirby and Kaneda, 2005). Research has shown that the use of health services varies according to the living environment. One such study found that accessibility of a general practice surgery varies according to the level of social disadvantage of local catchment areas (Hyndman and Holman, 2001). There are fewer doctor-hours

 Corresponding author. Tel.: +33 1 42 17 72 96.

E-mail address: [email protected] (H. Charreire). 1353-8292/$ - see front matter & 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.healthplace.2008.07.005

of service and lower provision of same-day services in disadvantaged areas, notwithstanding a seemingly adequate supply of physical resources. Studies on perinatal health have shown that poor prenatal care (late participation and irregular consultation) increases risks of prematurity, hypotrophy and/or perinatal death (Herbst et al., 2003; Vintzileos et al., 2002). Prematurity and hypotrophy are currently the main causes of infant mortality, handicaps and longterm deficiencies (Expertise Collective De L’inserm, 2004; Herbst et al., 2003; Vintzileos et al., 2002). Various studies undertaken in France and other countries have shown the existence of inequalities in access to healthcare and in the state of neonates’ health, corresponding to the mother’s level of education or professional category (Blondel et al., 2005; Devlieger et al., 2005; Canterino et al., 2004; Kaminski et al., 2000). Yet individual factors are not the only determining factors. It is known that the living conditions of a pregnant woman also influence the take-up of healthcare services (Lia-Hoagberg et al., 1990; Roberts, 1997; Delvaux et al., 2001; Buka et al., 2003; Bell et al., 2006; Grady, 2006). Research in Canada has established significant variations in care use, which persisted after control for individual-level factors (Dunlop et al., 2000; Newbold et al., 1995; Law et al., 2005). A large number of studies have used geographic tools to analyze spatial inequalities in perinatal care (Reader, 2001; Sridharan et al., 2007; Wu et al., 2004). To our knowledge only one has proposed a spatial approach to unequal access to prenatal services (Mclafferty and Grady, 2004). In that study, the authors present a Geographical Information System (GIS) analysis of prenatal care need and clinic services for low-income mothers in Brooklyn (New York) and analyze the association between

ARTICLE IN PRESS H. Charreire, E. Combier / Health & Place 15 (2009) 412–419

prenatal clinic density and women’s late use of prenatal care. This spatial approach enables us to identify high-priority areas with a high density of needy women and a low clinic density; in short, areas where new prenatal clinics are needed. It is then necessary, as for risk factors, to establish the geographic location of those territories in which the most prenatal problems occur, in order to know where to intervene. In our study, we posit that particular spatial configurations of access to care in a territory are reflected in the presence of areas where difficulties of access to care continue to exist. We try to establish whether the areas in which difficulties are observed overlap with those that have several risk factors (absence of care services, social precariousness and physical barriers) or with those that have a single sufficiently great barrier to access to care. The objectives of our study are (1) to identify and map the spatial patterns of prenatal care, and thus to determine whether and where there are high (or low) levels of poor prenatal care; (2) to observe, by means of an exploratory approach, whether areas of poor prenatal care are geographically linked to an absence of care services, to the presence of physical obstacles (urban infrastructure) or to social characteristics of the population (precariousness). We have combined spatial analysis (GIS) with an empirical approach to study the link between poor prenatal care, physical barriers, location of prenatal services and a neighborhood’s SES.

Study area and geographical scale of analysis The study was carried out in the district of Seine-Saint-Denis, north-east of Paris, France. With a surface area of 236 km2, it is one of the smallest districts in France, yet ranks seventh for population density: 5849 inhab/km2 (1.38 million inhabitants in 1999). This district was chosen for analysis because of the diversity of both its population (age, education level, country of birth) and its housing: a combination of old town centers, rundown apartment blocks, areas with individual houses and large council housing estates. In less than a century some farming villages on the outskirts of Paris became industrial and residential areas (individual houses and apartments) cris-crossed by main communication networks. There are numerous health services, with diversified prenatal care services. The district has 91 ‘‘maternal protection’’ centers—hereafter referred to as PM— offering free consultations for pregnant women (even those without a residence permit); 15 maternity clinics, five of which are state-owned; 1175 general practitioners, and 39 gynecologists in private practice (Dragos et al., 2002). The geographical scale is the ‘‘IRIS 2000’’, the smallest infraurban entity for which data from the population census are available to the general public. The IRIS scale is equivalent to that of a neighborhood, theoretically with inhabitants between 1800 and 5000 (INSEE, 1999). Seine-Saint-Denis consists of 613 IRIS.

Measures Three sets of data are used in this research: prenatal care data drawn from health certificates from 1999 to 2001; the 1999 national population census; and the geographical location (addresses) of healthcare services obtained from administrative data banks. Prenatal health data Data base used In France every neonate has medical tests during the first week after birth. A health certificate called the CS8 is then issued on the

413

eighth day. This certificate contains data on the infant’s condition at birth (weight, term) and the prenatal care received (number of consultations). For births in the Seine-Saint-Denis district, an experimental certificate is used, containing more information on the mother, especially the date of the first consultation during the pregnancy. For women who gave birth outside the district but live in Seine-Saint-Denis, the CS8 used did not contain data on the date of the first consultation, and few data were available on the number of consultations. Consequently, the level of missing data on prenatal care was very high in the communes bordering Paris, with up to 32%. We therefore limited our analysis to the 19 communes in which the percentage of missing data was o8.5 (median of the rate of missing data for the entire district). This zone, which corresponds to the final sample, has 30,338 CS8 (that is, 30,338 women who gave birth in the period from 1999 to 2001) in 285 IRIS. Definition of poor prenatal care Poor prenatal care was evaluated by combining two types of data from the CS8: number of medical visits during the pregnancy and date of the first consultation. Prenatal care is considered to be poor when the pregnancy is registered during the last 3 months or when there are fewer than four medical visits during the entire pregnancy (Blondel et al., 2005). ‘‘Poor prenatal care’’, as defined above, is a dichotomous variable with 1 ¼ poor prenatal care. Socio-economic status of neighborhoods All the information used to characterize the social context of the area was drawn from the 1999 French census data. We selected those data which were considered to be markers of risks in prenatal care in the national perinatal survey (Blondel et al., 1998): percentage of single-parent families, mother’s level of education (percentage of women without high-school education), immigrant families (percentage of foreign-born), male and female unemployment (percentage of economically active males and females seeking or waiting to start work), car ownership (percentage of households which do not own a car). To characterize the context of the neighborhood, variables describing the quality of the residence were also included: percentage of residences built before 1949 (when the use of lead-based paint was banned in France) and percentage of toilets outside the residence. Data on healthcare services The addresses of the prenatal health services (gynecologists, GPs, maternity clinics) were taken from the national reference file on health professionals (ADELI and FINESS). The addresses of PM centers were drawn from the PM database for the Seine-SaintDenis district.

Methods Characterizing the life profiles of the population: deprivation index An approach in terms of deprivation was used to analyze social inequalities. Townsend (1987) developed this approach to identify difficulties, disadvantages or deprivation in living conditions, and synthesized them in a deprivation score. Carstairs and Morris also proposed a deprivation index that likewise measures the living conditions (Morris and Carstairs, 1991; Carstairs, 1995).

ARTICLE IN PRESS 414

H. Charreire, E. Combier / Health & Place 15 (2009) 412–419

To establish our deprivation index, we compute two indexes: a ‘‘social deprivation index’’ and a ‘‘housing deprivation index’’:

 Social deprivation refers to single parenting, education,



immigrant families, unemployment and car ownership. To compute a ‘‘social deprivation index’’, all variables were standardized by subtracting their mean and dividing by their standard deviation (Proc score, SAS 9.1). The resulting products were then summed to produce the value of the deprivation index (Carstairs, 2000). The IRIS was divided into five classes corresponding to the distribution of the index in quintiles. The first class (1st quintile) corresponded to the most privileged neighborhoods and the last quintile (class 5) to the most under-privileged. To identify the old rundown housing, we used the same method to calculate a ‘‘housing deprivation index’’. Housing deprivation was mainly associated with the quality of the residence (date of construction and toilets outside the residence). Like the social deprivation index, the housing deprivation index was divided into five classes based on the quintiles. Class 5 (the 5th quintile) corresponded to neighborhoods with the most old housing and dwellings without toilets, while class 1 (1st quintile) corresponded to neighborhoods with recent housing and few dwellings without toilets.

To identify populations that were under-privileged and those that were not, in both dimensions—social and housing—, we finally cross-compared the two indexes divided into five classes. To have a large enough number of individuals in each class, we grouped together the classes whose population characteristics were similar (Fig. 1(a)) using the model created by Pampalon and Raymond (2000, 2003). We thus obtained the final deprivation index divided into five classes: class 1 represents the most privileged population living in the more recent residential areas of Seine-Saint-Denis, while class 5 represents the most underprivileged section of the population, living in IRIS with a high proportion of the oldest and most dilapidated housing. The IRIS in class 4 (Fig. 1(a)) correspond to the large council housing estates

built recently, on which a socially disadvantaged population is concentrated, whereas those of class 3 correspond to rehabilitated old neighborhoods, whose inhabitants have a higher socioeconomic level than that of the inhabitants of classes 4 and 5. Spatial effects and exploratory spatial data analysis Spatial autocorrelation can be defined as the coincidence of value similarity and locational similarity (Anselin, 2001). Therefore, there is positive spatial autocorrelation when high or low values of a random variable tend to be spatially clustered, and negative spatial autocorrelation when geographical areas tend to be surrounded by neighbors with very dissimilar values. To analyze the geographic dimension of the spatial patterns of prenatal care, we used the techniques of exploratory spatial data analysis (ESDA). These techniques serve to describe spatial distributions (clusters or dispersions) in terms of spatial association patterns such as global spatial association and local spatial association (Anselin, 1995; Anselin et al., 2006; Goovaerts and Jacquez, 2004; Jacquez and Greiling, 2003). These patterns are associated with a spatial weight matrix, where each unit is connected to a set of neighboring sites. In other words, spatial connectivity is incorporated by means of a spatial weight matrix (Anselin, 1995). In this paper, a matrix of distances between the IRIS centroids was used to model relations between the spatial units (IRIS) (Gatrell and Bailey, 1996; Bavaud, 1998). The results presented are those obtained with a matrix of 2.5 km (mean distance between the IRIS, for the entire district). Global spatial autocorrelation We first considered global spatial autocorrelation of the rates of poor prenatal care, the measurement of which is usually based on Moran’s I statistic (Gatrell, 1979; Griffith, 1992; Anselin, 1995; Jacquez and Greiling, 2003). The values of I indicate positive or negative spatial autocorrelation. We then studied the bivariate spatial relationship between poor prenatal care and deprivation, using Moran’s bivariate. This information concerning the global spatial autocorrelation had to be complete; in particular, spatial clustering of high values needed to be distinguished from spatial clustering of low values since we were interested mainly in the former when identifying poor prenatal care areas. We needed to assess local spatial autocorrelation in our study. Local indicator of spatial autocorrelation (LISA) Moran’s diagram shows the types of spatial relationship between a unit of place and the neighboring units. This allows us to visualize four types of local spatial associations between an observation point and its neighbors, each of them being located in a quadrant of the scatterplot. In our analysis each neighborhood can, therefore, be characterized by one of the following associations:

 High High (HH): high rate of poor prenatal care in an IRIS, and    Fig. 1. Deprivation variation per IRIS in the Seine-Saint-Denis district: (a) classes of ‘‘social index’’ and ‘‘housing index’’ and (b) map of deprivation area (1999).

neighboring IRIS also have high rates of poor prenatal care (positive association). High Low (HL): high rate of poor prenatal care in an IRIS, whereas neighboring IRIS have low rates of poor prenatal care (negative association). Low Low (LL): low rate of poor prenatal care in an IRIS, and neighboring IRIS also have low rates of poor prenatal care (positive association). Low High (LH): low rate of poor prenatal care in an IRIS, whereas neighboring IRIS have high rates of poor prenatal care (negative association).

ARTICLE IN PRESS H. Charreire, E. Combier / Health & Place 15 (2009) 412–419

In short, the set of neighboring HH and HL areas can be considered as the areas in which women receive poor prenatal care. All these operations were performed with the GEODA free software (Anselin et al., 2006).

Measuring physical accessibility We geocoded each of the prenatal services to a point location based on street address, and measured the physical (Euclidian) distances between each IRIS centroid and the closest service, for the four types of health service (with ArcView 9.1). This distance represents a proxy of the minimum distance that a woman must travel (centroid of the IRIS) between her home and each type of health service.

Result Spatial characteristics of populations and housing Characteristics of the deprivation index classes We first used the deprivation index to analyze the social characteristics and housing of the various neighborhoods (IRIS). The results are presented in Table 1. Classes 4 and 5 represent those neighborhoods which combine indicators of a low socio-economic level (unemployment among men and women, level of qualifications, etc.) with a large proportion of immigrants. The analysis of the deprivation index in these two components (social and housing) reveals two types of precariousness in the IRIS: precariousness in old housing (class 5) where the proportion of old housing is over 30%, and precariousness in recent housing (class 4) where the proportion of old housing accounts for only 6% of all housing. Class 1 has the lowest rates for all the variables. It represents the most privileged profiles, and the housing is mostly recent. Classes 2 and 3 represent profiles of a high socio-economic level, with a difference in the type of housing between these two classes. We located the deprivation index obtained for each IRIS on the map of the district, and Fig. 1(b) illustrates socio-economic inequalities in the study area. Whereas the IRIS of classes 1, 2 and 3 are spread over the entire territory, without any particular spatial configuration being visible, the IRIS in class 5 can be grouped into several clusters. In the eastern part of the study area, we note some class 4 IRIS clusters (under-privileged neighborhoods with recent housing) surrounded by neighborhoods with lower class in the deprivation index (high socio-economic profiles).

415

Spatial distribution of women with inadequate prenatal care The result of Moran’s test for spatial autocorrelation is I ¼ 0.1355 (p ¼ 0.0001, pseudo-significance values based on a permutation approach, (Anselin, 1995). Moran’s I statistic, calculated by means of the distance matrix, shows that the location of poor prenatal care per IRIS is spatially autocorrelated. This means that the IRIS which have a high rate of poor prenatal care tend to be located close to other IRIS with a high rate of poor prenatal care, more often than if this distribution was random. The converse is also true, since those IRIS with low rates are usually adjacent to the IRIS with the lowest rate (Fig. 2). In addition, the bivariate Moran’s I statistic for spatial correlation between poor prenatal care and deprivation is 0.024 (po0.05), indicating a significant positive spatial relationship between poor prenatal care and deprivation of IRIS. According to the results obtained from the LISA statistics, the 285 IRIS are distributed as follows (Table 2): 19 HH-type (high level surrounded by high levels), 32 LL-type (low level surrounded by low levels), 8 LH-type (low level surrounded by high levels), 12 HL-type (high level surrounded by low levels) and 214 NS-type (no significant clustering). Table 2 sums up the characteristics of the different types obtained from these calculations, as well as the proportion of IRIS concerned by each type of spatial grouping. The mapped results of the calculations of the statistics (LISA) are presented in Fig. 3. Of the 285 IRIS studied over 3 years, the rate of poor prenatal care is 8.5%. For these pregnancies, the local pattern of spatial association at 0.001% reflects a global tendency for positive autocorrelation. The distribution, between the associations of HH-type and those of LL-type is similar since, for example, 49.5% of the positively autocorrelated IRIS are of the HH-type. IRIS of the HH-type is concentrated in the northern part of the district under study. Neighborhoods of the LL-type are grouped together in the southern zone. These results show wide spatial disparities in prenatal care in the Seine-Saint-Denis district. The results presented in Table 2 show that the Euclidian distances between the womens’ homes (centroid of IRIS 1) and the health services (centroid of IRIS 2) are short. Even if some statistical distances exist between the classes outside maternity institutions, the physical distances are always very short throughout the district, especially for general practitioners and PM centers, the two most prevalent types of service. Analysis of particular cases (territorial zoom): contributions and limits of the geographical method This section presents the clusters in which we carried out field research by investigating geographical obstacles and the spatial

Table 1 Social and housing neighbourhood characteristics by class of deprivation index Number of IRIS

Class 1 74

Rates of

%

95% CI

%

95% CI

%

95% CI

%

95% CI

%

95% CI

Social deprivation index No higher education Single-parent family Unemployment of men Unemployment of women Foreign population

77,9 13,6 10,1 11,5 16,0

76,37–79,41 12,37–14,89 9,34–10,85 10,83–12,14 15,06–17,01

81,0 16,5 13,7 14,0 20,3

80,11–81,96 15,55–17,51 12,91–14,44 13,45–14,60 19,36–21,28

78,2 13,7 13,1 13,7 20,1

77,26–79,13 13,01–14,94 12,57–13,60 13,14–14,19 19,27–20,90

87,9 23,1 23,1 24,2 30,0

87,29–88,45 22,34–24,05 22,29–23,94 23,30–31,04 28,90–31,04

83,1 19,0 23,5 22,6 34,4

81,82–84,29 17,91–20,16 22,07–24,93 21,45–23,82 32,29–36,59

7,0 1,5

5,71–8,31 1,39–1,64

10,8 2,0

9,26–12,34 1,85–2,06

32,1 2,9

30,33–33,77 2,72–3,04

5,5 1,7

4,33–6,57 1,59–1,79

36,5 4,3

33,04–40,03 3,91–4,74

Housing deprivation index Built before 1949 Outside toilets

Class 2 130

Class 3 161

Class 4 162

Class 5 82

ARTICLE IN PRESS 416

H. Charreire, E. Combier / Health & Place 15 (2009) 412–419

1.0 0.8

LH

HH

LL

HL

Spatial lag of prenatal care

0.6 0.4 0.2 0.0 -0.2 -0.4 -0.6 -0.8 -1.0 -1.2

-3

-2

-1 0 1 Standardized value of prenatal care

2

3

Fig. 2. Moran scatterplot.

Table 2 Poor prenatal care and Euclidian distances (meters) to prenatal care services in the IRIS types obtained by LISA statistics LISA statistic level types Number of IRIS

HH 19

LL 32

Antenatal care Total of pregnancies N %

2856 365 12.2

3260 52 4.7

863 161 5.7

16.8 6.4

7.9 1.7

7.6 2.9

Poor antenatal care Max Min

Euclidian distances (meters)a General practitioner Mean 107 Max 621

LH 8

HL 12

133 109 10

14.8 8.3

NS 214

Total 285

22226 1884 8.4

30338 2571 8.5

16.3 1,4

185 864

91 386

165 561

168 1587

Gynaecologist Mean Max

753 1485

846 2114

1123 1821

819 1268

1305 4371

PMI centre Mean Max

510 1045

925 2125

768 1557

731 1583

701 3020

Maternity Mean Max

1833 2903

2344 3747

1595 2700

2134 3135

1466 3904

a

Minimum is 0.

organization of health services. We opted for qualitative ‘‘field’’ work rather than integrating distance calculations into the disadvantage index as proposed for the calculation of the disadvantage index in rural areas (Niggebrugge et al., 2005; Jordan et al., 2004). In a highly urbanized area such as the SeineSaint-Denis district, where social inequalities are large, the combination of social particularities and characteristics relative to urban infrastructure complicates the notion of distance. Although distances are perceived as short by a well-off family with one or more cars, they may seem long for pregnant women who have to walk. A road may be seen as an opening or, on the

Fig. 3. LISA cluster map of prenatal care Seine-Saint-Denis district (1999–2001).

contrary, as a barrier that isolates. These barrier effects are not quantifiable, and in urban areas where distances are theoretically short they cannot be reduced to a measurement of the Euclidian distance.

ARTICLE IN PRESS H. Charreire, E. Combier / Health & Place 15 (2009) 412–419

417

Fig. 4. Location of prenatal services and areas with poor prenatal care (1999–2001).

Our GIS-based analysis of populations’ living environment in each neighborhood, of the distribution of the healthcare services, and of the urban infrastructure enabled us to reach three main conclusions (Fig. 4). (1) Spatial and social barriers to prenatal care: The results of the LISA revealed neighborhoods, where women experience barriers to access to prenatal care even though they live close to a GP’s surgery. These types of neighborhood are characterized by a type4 population with a precarious social situation. We can therefore posit that in these sectors women primarily use the PM centers, which correspond to a dispensary-type service where free consultations are organized with interpreters. For example, in Clichy-sous-Bois these women do not have cars and the topography (uphills) and road engineering (detour to cross at a traffic-light) increase the real access time. During our fieldwork we noted that these PM centers are not served by public transport (busses). It is almost three quarter of an hour’s walk for women in these neighborhoods to get to the PM center situated o1 km away in Euclidian distance. Moreover, these centers, the only alternative to the private offer, are saturated and the medical staff informed us that the waiting time for a consultation could be up to several weeks. This phenomenon of saturation of the PM centers can also be observed in the IRIS of Montfermeil and Livry-Gargan. In Montfermeil the neighborhoods concerned by difficulties of access to prenatal care are also characterized by the precarious situations of their inhabitants, and are physically and socially separated from the rest of the town. Urban layout, combined with social particularities, is therefore a factor of inequality of access to care. Even if physical distances are short, being close to one type of service is not always synonymous with accessibility. (2) The absence of neighborhood care—both PM centers and GPs’ surgeries—in geographic enclaves characterized by a population in a precarious situation: For example, Gagny, a neighborhood with council housing, is cut off from adjacent neighborhoods by a railway line and motorway which are real geographical obstacles here as well. The only PM center in the town and the general practitioners are grouped together in the north-western part of the town, and neighboring communes have few prenatal care services. In the case under study, we posited the existence of a

problem of access to care related to the prenatal care system (availability, proximity). This absence of healthcare can also be observed in the neighborhoods concerned by poor prenatal care, in the towns of Villemomble, Le Raincy and Livry-Gargan. (3) Identification of an artifact (Northern part of the district): In the northern part of the study area we observe a concentration of HH-type neighborhoods. These IRIS correspond to disadvantaged areas (class 4 of the ‘‘deprivation index’’) in a recent habitat of the same type as that described in the town Clichy-sous-Bois (dilapidated housing estates). Neighborhood healthcare services are present since there are 10 GPs, two PM centers and one maternity ward. The existence of women with poor prenatal care in this part of the district is related neither to a geographical problem (spatial impediments) nor to the organization of prenatal care. In view of this particular situation, we posited the existence of an artifact related to the way in which CS8 forms were filled in at the maternity ward, where the vast majority of women in this area gave birth. To confirm this hypothesis we held interviews with professionals in the field. During these interviews it was confirmed that the services delivered by the PM centers fulfilled the population’s needs and that the women encountered no spatial barriers hindering their physical access to such centers. The concentration of women who received poor prenatal care revealed a particular coding of CS at the maternity ward. The staff who filled in the CS indicated not the date on which the pregnancy was first reported but that of the first examination at the hospital, as well as the number of consultations at the hospital only. This coding resulted in inaccurate data on prenatal care. The rate was, therefore, overestimated in those IRIS where women gave birth in the maternity ward in the northern part of the territory under study, for the period 1999–2001.

Discussion Our study shows that despite proximity and diversity of the services and the freedom that women have to choose their medical practitioners and health services, we recorded poor prenatal care. Physical distances are short in this highly urbanized area and women in France are free to choose their doctor and the

ARTICLE IN PRESS 418

H. Charreire, E. Combier / Health & Place 15 (2009) 412–419

type of institution in which they receive prenatal care. They can consult doctors (GP, gynecologist) in private practice, private clinics, maternity wards in public hospitals or at PM centers (similar to a dispensary, providing free care). There is no zoning and, if they so wish, women can travel long distances to consult a particular practitioner (Combier et al., 2004). They nevertheless still have difficulties in access to services for prenatal care. The impact of social context has been translated into a disadvantage index based on the model proposed by R. Pampalon using census variables available in France. Without any income data on an IRIS scale for the period of our study, we exploited proxies such as unemployment amongst men and women. Likewise, only data on foreign birth (yes/no) are available in the survey, without any information on the country of birth. This translation of social context is completed by housing data. From the date of construction of the dwelling we can assess the risk of the presence of leaded paint in housing (in France, use of leaded paint was banned from 1949) and distinguish between different types of dwelling (old individual houses or recent collective housing). We used the spatial analysis and mapping capabilities of GIS to describe the uneven geographical distribution of poor prenatal care. Identification of the geographic zones of poor prenatal care on an IRIS scale, that is, small areas, was achieved by means of a specific method which takes into account phenomena of spatial autocorrelation (global and local): the ESDA. This spatial analysis tool, first used in research in criminology (Morenoff, 2003; Messner and Anselin, 2004) and spatial econometrics (Le Gallo and Ertur, 2003; Guillain and Le Gallo, 2007; Guillain et al., 2006), enabled us, for each spatial unit studied, to take into account the level of indicators identified in the adjacent spatial units. It also enabled us to classify the IRIS in relation to their rate of poor prenatal care (high rate, low rate and rate not significant in relation to the average) and the rate in neighboring IRIS. The analysis in this paper has used a distance matrix as a spatial weight matrix to limit biases due to the heterogeneity of sizes and shapes of neighborhoods (IRIS). The IRIS identified as having no population (n ¼ 10) have been aggregated with the closest IRIS, to avoid ‘‘gaps’’ in the map and a bias in the calculation of distance. As described in Anselin et al. (2007): Each row i of matrix w has elements wij corresponding to the columns j [y]. In practice, it is near impossible to choose a ‘‘best’’ weight matrix and typically one assesses the sensitivity of the results to the selection of weights. The robustness of the results has been verified by means of a distance matrix and contiguity matrixes (‘‘rook’’ contiguity (only pure borders) or ‘‘queen’’ contiguity (both borders and common vertices) Geoda Software). The map of the different classes (Fig. 3) reveals clusters of IRIS with high rates of poor prenatal care (HH-type) or else IRIS in which poor prenatal care is less frequent than the average (LL-type). It also shows the presence of IRIS with a high rate of poor prenatal care in LL-type clusters (HL-type) or, on the contrary, IRIS with a low rate in areas with a high rate (LH-type). These inclusions, whether HL-type or LH-type, confirm that in urban contexts the physical distance of access to health services is not the preponderant factor of poor prenatal care. Combine spatial analysis and fieldwork Our study shows the limits of exclusive use of GIS as a decision-making and sanitary planning tool. For the deprivation index, IRIS class 4 neighborhoods are those with a combination of socio-economic risk factors (Table 1), characterized mostly by

council housing built in the 1960s–70s to house the populations of slums. Today this housing (which had modern conveniences, especially toilets and bathrooms) is severely run down in certain neighborhoods where living conditions are difficult. Census data do not enable us to translate this deterioration of the housing, as no variable has been constructed to characterize or quantify the quality of recent housing. Only by means of fieldwork can the researcher be fully aware of the deteriorated state of the residence. Fieldwork is likewise necessary to identify and make an inventory of facilities and infrastructures (such as roads and railways), which are actually spatial barriers to access to healthcare services, and to understand women’s spatial practices. Knowing a population and its spatial base (place of residence) in detail affords a better grasp of the disparities in health as all contextual data are taken into account, including social context and the distribution of healthcare. This knowledge is based on field research in the territory under consideration, which enables us to understand the problems of local planning and development. Spatial analyses provide the tool for detecting health inequalities, but for a full understanding, a field study is required in which the spatial organization of the territory is analyzed in detail. By linking up the need for care, the quantity and quality of services offered, and the spatial base, the inequalities in health or access to care are highlighted. Essential questions raised by medical professionals, concerning the equity and efficiency of the healthcare system, are partially answered. It is therefore indispensable to decipher the town, its organization, and its facilities and infrastructure, which can be obstacles and create inequalities in the access to healthcare. The approach adopted in our research enables us to isolate these local particularities and to understand them better.

Acknowledgements This study received the financial support of the Conseil regional d’Ile-de-France. French census data were provided by the Maurice Halbwachs Center. The authors would like to thank Julie LeGallo and Rachel Guillain for their help and for reviewing this paper. References Anselin, L., 1995. Local indicator of spatial association—LISA. Geographical Analysis 27, 93–115. Anselin, L., 2001. Spatial econometrics. In: Baltagi, B. (Ed.), Companion to Econometrics. Basil Blackwell, Oxford. Anselin, L., Syabri, I., Kho, Y., 2006. GeoDa: an introduction to spatial data analysis. Geographical Analysis 38, 5–22. Anselin, L., Sridharan, S., Gholston, S., 2007. Using exploratory spatial data analysis to leverage social indicator databases: the discovery of interesting patterns. Social Indicators Research 82, 287–309. Bavaud, F., 1998. Models for spatial weights: a systematic look. Geographical Analysis 30, 153–171. Bell, J.F., Zimmerman, F.J., Almgren, G.R., Mayer, J.D., Huebner, C.E., 2006. Birth outcomes among urban African–American women: a multilevel analysis of the role of racial residential segregation. Social Science and Medicine 63, 3030–3045. Blondel, B., Marshall, B., 1998. Poor antenatal care in 20 French districts: risk factors and pregnancy outcome. Journal of Epidemiology and Community Health 52, 501–506. Blondel, B., Norton, J., Du Mazaubrun, C., Bre´art, G., 1998. Enqueˆte nationale pe´rinatale 1998. Paris Ministe`re de l’emploi et de la solidarite´. Blondel, B., Supernant, K., Du Mazaubrun, C., Breart, G., 2005. Enqueˆte nationale pe´rinatale 2003: situation en 2003 et e´volution depuis 1998. Paris, France, Ministe`re du Travail et des Affaires sociales. Direction Ge´ne´rale de la Sante´ (DGS), INSERM U 149. Buka, S.L., Brennan, R.T., Rich-Edwards, J.W., Raudenbush, S.W., Earls, F., 2003. Neighborhood support and the birth weight of urban infants. American Journal of Epidemiology 157, 1–8. Canterino, J.C., Ananth, C.V., Smulian, J., Harrigan, J.T., Vintzileos, A.M., 2004. Maternal age and risk of fetal death in singleton gestations: USA, 1995–2000. Journal of Maternal-Fetal Neonatal Medicine 15, 193–197.

ARTICLE IN PRESS H. Charreire, E. Combier / Health & Place 15 (2009) 412–419

Carstairs, V., 1995. Deprivation indices: their interpretation and use in relation to health. Journal of Epidemiology and Community Health 49 (Suppl. 2), S3–S8. Carstairs, V., 2000. Socio-economic factors at area level and their relationship with health. Spatial Epidemiology: Methods and Application, 58–67. Combier, E., Zeitlin, J., De Courcel, N., Vasseur, S., Lalouf, A., Amat-Roze, J.M., De Pouvourville, G., 2004. Choosing where to deliver: decision criteria among women with low-risk pregnancies in France. Social Science and Medicine 58, 2279–2289. Delvaux, T., Buekens, P., Godin, I., Boutsen, M., 2001. Barriers to prenatal care in Europe. American Journal of Preventive Medicine 21, 52–59. Devlieger, H., Martens, G., Bekaert, A., 2005. Social inequalities in perinatal and infant mortality in the northern region of Belgium (the Flanders). European Journal of Public Health 15, 15–19. Dragos, S., Topuz, B., Rvivera, L., 2002. Etat des lieux de l’offre de soins me´dicale ‘‘en ville’’ pour les enfants et les femmes (1999–2001). Conseil Ge´ne´ral de la Seine-Saint-Denis, Direction de l’Enfance et de la Famille, Services de Protection Maternelle et Infantile. Dunlop, S., Coyte, P.C., Mcisaac, W., 2000. Socio-economic status and the utilisation of physicians’ services: results from the Canadian national population health survey. Social Science and Medicine 51, 123–133. Expertise Collective De L’inserm, 2004. De´ficiences et handicaps d’origine pe´rinatale-De´pistage et prise en charge. INSERM, Paris. Gatrell, A., 1979. Autocorrelation in spaces. Environment and Planning A 11, 507–516. Gatrell, A.C., Bailey, T.C., 1996. Interactive spatial data analysis in medical geography. Social Science and Medicine 42, 843–855. Goovaerts, P., Jacquez, G.M., 2004. Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York. International Journal of Health Geographics 3, 14. Grady, S., 2006. Racial disparities in low birthweight and the contribution of residential segregation: a multilevel analysis. Social Science and Medicine, 63. Griffith, D.A., 1992. What is spatial autocorrelation? L’Espace Ge´ographique 21, 265–280. Guillain, R., Le Gallo, J., 2007. Agglomeration and dispersion of economic activities in Paris and its surroundings: an exploratory spatial data analysis. LEG—Document de Travail—Economie. Guillain, R., Le Gallo, J., Boiteux-Orain, C., 2006. Changes in spatial and sectoral patterns of employment in Ile-de-France, 1978–97. Urban Studies 83, 2075–2098. Herbst, M.A., Mercer, B.M., Beazley, D., Meyer, N., Carr, T., 2003. Relationship of prenatal care and perinatal morbidity in low-birth-weight infants. American Journal of Obstetrics and Gynecology 189, 930–933. Hyndman, J.C., Holman, C.D., 2001. Accessibility and spatial distribution of general practice services in an Australian city by levels of social disadvantage. Social Science and Medicine 53, 1599–1609. Insee, 1999. (French Institute for Statistics and Economic Studies), definition of the census of the population: /http://www.insee.fr/fr/nom_def_met/definitions/ html/ilots-regr-pour-inf-stat-2000.htmS. Jacquez, G.M., Greiling, D.A., 2003. Local clustering in breast, lung and colorectal cancer in Long Island, New York. International Journal of Health Geographics 2, 3. Jordan, H., Roderick, P., Martin, D., 2004. The index of multiple deprivation 2000 and accessibility effects on health. Journal of Epidemiology and Community Health 58, 250–257.

419

Kaminski, M., Blondel, B., Saurel-Cubizolles, M., 2000. La sante´ pe´rinatale. In: Leclerc, A., et al. (Eds.), Les ine´galite´s sociales de sante´. La de´courverte Coll. Recherches, Paris. Kirby, B., Kaneda, T., 2005. Neighborhood socioeconomic disadvantage and access to health care. Journal of Health and Social Behaviour 46, 15–31. Law, M., Wilson, K., Eyles, J., Elliott, S., Jerrett, M., Moffat, T., Luginaah, I., 2005. Meeting health need, accessing health care: the role of neighbourhood. Health Place 11, 367–377. Le Gallo, J., Ertur, C., 2003. Exploratory spatial data analysis of the distribution of regional per capita GDP in Europe, 1980–1995. Papers in Regional Science 82, 175–201. Lia-Hoagberg, B., Rode, P., Skovholt, C.J., Oberg, C.N., Berg, C., Mullett, S., Choi, T., 1990. Barriers and motivators to prenatal care among low-income women. Social Science and Medicine 30, 487–495. Mclafferty, S., Grady, S., 2004. Prenatal care need and access: a GIS analysis. Journal of Medical Systems 28, 321–333. Messner, S., Anselin, L., 2004. Spatial analyses of homicide with areal data. In: Goodchild, M., Janelle, D. (Eds.), Spatially Integrated Social Science. Oxford University Press, New York. Morenoff, J.D., 2003. Neighborhood mechanisms and the spatial dynamics of birth weight. AJS 108, 976–1017. Morris, R., Carstairs, V., 1991. Which deprivation? A comparison of selected deprivation indexes. Journal of Public Health Medicine 13, 318–326. Nelson, D.E., Thompson, B.L., Bland, S.D., Rubinson, R., 1999. Trends in perceived cost as a barrier to medical care, 1991–1996. American Journal of Public Health 89, 1410–1413. Newbold, K.B., Eyles, J., Birch, S., 1995. Equity in health care: methodological contributions to the analysis of hospital utilization within Canada. Social Science and Medicine 40, 1181–1192. Niggebrugge, A., Haynes, R., Jones, A., Lovett, A., Harvey, I., 2005. The index of multiple deprivation 2000 access domain: a useful indicator for public health? Social Science and Medicine 60, 2743–2753. Pampalon, R., Raymond, G., 2000. A deprivation index for health and welfare planning in Quebec. Chronic Diseases in Canada 21, 104–113. Pampalon, R., Raymond, G., 2003. Indice de de´favorisation mate´rielle et sociale: son application au secteur de la sante´ et du bien-eˆtre. Sante´, Solidarite´ et Socie´te´ 2, 191–208. Reader, S., 2001. Detecting and analysing cluster of low-birth weight incidence using exploratory spatial data analysis. Geojournal 53, 149–159. Roberts, E.M., 1997. Neighborhood social environments and the distribution of low birthweight in Chicago. American Journal of Public Health 87, 597–603. Sridharan, S., Tunstall, H., Lawder, R., Mitchell, R., 2007. An exploratory spatial data analysis approach to understanding the relationship between deprivation and mortality in Scotland. Social Science and Medicine 65, 1942–1952. Townsend, P., 1987. Deprivation. Journal of Social Policy 16, 125–146. Vintzileos, A., Ananth, C.V., Smulian, J.C., Scorza, W.E., Knuppel, R.A., 2002. The impact of prenatal care on postneonatal deaths in the presence and absence of antenatal high-risk conditions. American Journal of Obstetrics and Gynecology 187, 1258–1262. Wu, J., Wang, J., Meng, B., Chen, G., Pang, L., Song, X., Zhang, K., Zhang, T., Zheng, X., 2004. Exploratory spatial data analysis for the identification of risk factors to birth defects. BMC Public Health 4, 23.